I’m more comfortable with the latter–having been a student (and victim) of it for more than a decade–but the the process of theory building is a new concept. At least to me but also, I believe, to many. The belief that a theory is fully cooked when first conceived is not the way science developed and the idea that business management theories are singular ideas rather than processes is symptomatic of an immaturity in the field.

So here are the basics of theory building as put forward by Clay Christensen and David Sundahl:

Definition: A theory is a statement of what causes what, and why, and under what circumstances. A theory can be a contingent statement or a proven statement. That is all.

Many managers shy away from using the word “theory” because it is associated with the term theoretical which suggests impractical. But managers use theory every day. They make decisions on some basis of cause and effect, often without being specific about their reasoning.

Process: First comes observation. Second, description. Third categorization. Fourth comes analysis and a statement of what causes what and why. This analysis can be simply an observation of a pattern or a more rigorous correlation analysis.

But that’s not the end of the process. The causal statement needs to be tested by predictions whose validity is tested with further observations and confirmation or denial of the statement. If the statement is denied we need to decide if it’s an anomaly that expands the theory or whether it contradicts the theory making it less useful.

The anomaly allows a new categorization to take shape. Getting the categories right is the key to the usefulness of the theory. The discovery of anomalies can thus make a theory stronger. The discovery of anomalous phenomena is the pivotal element in the process of building an improved theory.

This iteration between prediction/confirmation/anomaly handling can go for quite some time. As anomalies are accounted for on a regular basis then they can either be exhausted or depleted enough that a robust enough categorization emerges and the predictive power is nearly complete.

Example: In my reading of Apple’s financial statements I observed that Capital Expenditures were rising dramatically after the company began to sell iPhones. The observations were made over a few years. The pattern observed showed some correlation between spending and shipments of units.

The company’s spending was then compared with a group of other technology companies. These observations suggested that spending varied according to business model and strategy and that Apple seemed to be transitioning from one type of spending (on infrastructure) to another (on manufacturing equipment.)

Then a statement was made that Apple was using capital expenditures to not only ensure supply of components but also of component manufacturing equipment. This was borne of necessity but had the side effect of creating competitive advantage as its unibody devices and Macs were unique and differentiated.

As the more data came in, by the prediction was made that capital expenditures– which are incurred before production starts and which are pre-announced on a fiscal year basis — indicate new product ramps or new product introductions.

A few anomalies were experienced when spending increased but production didn’t and vice versa. These were studied and explained by shifts in technology (mainly screens) which required “out-of-phase” investment. Additionally, the companies in the cohort also varied their spending on the basis of opportunities in the short term.

As it stands, the theory that Apple uses capital investment in tooling to manage its quality and quantity of production and that in doing so it integrates deeply into its supply chain creating competitive lock-outs is holding up. It is not sufficiently precise to forecast actual production volumes for individual product lines but the growth in the business is broadly foretold by the growth in capital expenditures.

Indeed the share price generally reflects this:

Proposition: At a basic (micro) level, the process of theory building is something we do instinctively. We observe patterns, make statements that A causes B and carry on with the theory as an assumption. The challenge is more on a macro level. Few theories are built rigorously about the causes of success or failure of business systems. This includes understanding why large, powerful firms fail when confronted with small, weak competitors. Why, how and when industries disappear. How resources are allocated and how priorities are set. It’s as if individuals behave with far more intuitive insight than firms.

That is what must change.

Because firms are increasingly determining the prosperity and sustainability of nations and the world. We can’t afford mismanagement.

The counter-point to this quest is that large systems are intractable and business is inherently chaotic, unpredictable. It may be, but much of what we know as science today was once thought of as impossibly mysterious and unknowable. I have faith that as the physical universe, the affairs of men have laws which govern them.

Steve Jobs said death is the best thing in life. And yet we seek immortality, or at least life-extending technologies. What are the possibilities and implications of a salubrious app? Is life extension the next killer app[1]

When the iPhone 4S launched, one million units were pre-ordered and 4 million units were sold during its opening weekend. That made the daily rate during the 4S weekend 1.3 million units/day or one third faster than the pre-order rate of 1 million units/day.

When the iPhone 5 launched, 2 million were pre-ordered and “over” 5 million were sold during during the opening weekend. That made the daily rate during the launch weekend about 1.7 million which was about 15% slower than the pre-order rate. However, a few months later the 5 launched in China setting an opening rate of 2 million in three days or about 666k/day. Adding China’s rate to the Rest of World rate yields about 2.4 million/day or about 20% faster than the pre-order rate.

When the iPhone 6/6Plus launched, 4 million were pre-ordered and 10 million were sold during the opening weekend. That made a daily rate during the launch weekend about 3.3 million, again lower than the 4 million/day in pre-orders. However, just like the 5, the 6 launch excluded China. If we assume that a China launch would have run 30% faster than the 5 launch[1] then my estimate of launch performance for the iPhone range is shown in the graph below:

I included in the graph the various other launch volume data we have available.

I also included lines showing how pre-order volumes relate to weekend values for the products where we know both.

It therefore does not seem improbable that had China been available (and at the time when it will be) the iPhone launch weekend rate for the 6/6Plus combo would have been about 4 million/day. A rate consistent with the history for the product.

Notes:

Considering that this year China distribution includes China Mobile a 30% increase from two years ago is, in my opinion, conservative [↩]

How and why does Apple get paid for Apple Pay? Anders and Horace dive into the payments value chain and break it all down for you: whats in it for users, merchants, issuing banks and payment networks. What are the risks and opportunities for Apple? Is there a disruption about to happen?

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My hypothesis is that The Primary Cause for the shift of profits from Incumbents to Entrants has been the disruptive impact of a new input method.

It was a description of what I considered to be the “disruptive technology” which caused incumbents which had a “front-row seat” to the future of their industry to be completely displaced and marginalized by an entrant[1] with no discernible right to do what they did.

I illustrated what underpinned the sea change in the phone business via the slide that Steve Jobs used in the iPhone launch event:

I added the years when each input method was introduced and the platform/ecosystems created as a result. These new ecosystems were the primary cause for dramatic industry-sized shifts in profits.

Not coincidentally, during the 2014 Apple Watch launch, the presentation began[2] with a re-telling of the “mouse, click wheel and Multi-Touch” story.

Seven years later, the difference is that there is a new object added to the story. It answers the question that has been on my mind since that first post on revolutionary user interfaces was written: what will come next.

Now that we have an answer, the next step is to understand the new platform, its ecosystem; which industry will be affected and which incumbents will be displaced and to what degree will value be created beyond that which will be displaced.